Systems for detecting and analyzing target patterns in digital imagery have a wide variety of uses. One application of quantitative image analysis is the measurement of the staining intensity of cell components by specific receptors, also called biomarkers. For example, an image analysis system has been developed that segments cell membranes in digital images of cancer tissue that has been stained with a specific biomarker, such as the HER2 protein. The system then uses spatial recognition algorithms to quantify the membrane staining intensity, which provides an indication of the extent of cancer in the tissue being analyzed. Thus, the system is able to replace the conventional subjective grading procedures of a physician who visually grades a stained tissue sample with an automated quantification of membrane staining intensity.
Current automated staining intensity measurements, however, are performed only on tissue that has been stained with known biomarkers, to which the spatial recognition algorithms are tailored. But there are relatively few biomarkers whose staining behavior is known compared to the number of proteins and receptors that could potentially be used in the immunohistochemical study of tissue, such as cancer tissue.
A method is sought for evaluating the staining characteristics of proteins and receptors whose behavior in various types of tissue is not yet known in order to use those proteins and receptors as biomarkers.